Human lymphoid malignancies can be broadly divided into three groups: 1) those arising in B-cells; those arising in T-cells; and those arising in natural killer cells. Within the United States, approximately 85% of lymphoid malignancies are of B-cell origin. During normal B-cell development, after having undergone V(D)J recombination of the immunoglobulin (IG) locus in the bone marrow, immature B-cells move into secondary lymphoid sites, called germinal centers (GC) (Klein U, Dalla-Favera R., “Germinal centres: role in B-cell physiology and malignancy,” Nat Rev Immunol., 2008, 8(1):22-33). Responding to antigenic stimulation, the B-cells passage through the GC, and undergo somatic hypermutation, immunoglobulin class switching, and proliferation, finally being released from the GC either as memory cells or plasmablasts. The latter migrate to the bone marrow and differentiate into long-lived plasma cells. Thus, B-cell malignancies that arise in mature B-cells that have entered the GC share particular genetic features, such as clonal rearrangement and somatic hypermutation of the IGH gene.
Mature B-cell malignancies can include Hodgkin's lymphoma, non-Hodgkin's lymphoma (NHL), and multiple myeloma (MM). Table 1 below provides a listing of lymphoma types and frequencies in the U.S.
TABLE 1Frequencies of Lymphoma Types in the U.S.% of all~cases dx inGroupSubgrouplymphomas2008 in USHodgkin's Lymphoma11%8,220Non-Diffuse large B-cell lymphoma (DLBCL)28%20,600Hodgkin'sFollicular lymphoma (FL)20%14,550LymphomaMucosa-associated Lymphatic Tissue (MALT)6-7%5,610(NHL): B-celllymphoma*Chronic lymphocytic leukemia/small lymphocytic6%4,630lymphoma (CLL/SLL)Mantle cell lymphoma (MCL)5%3,970Mediastinal (thymic) large B-cell lymphoma (MLBL)2%1,580Lymphoplasmacytic lymphoma-Waldenstrom<2%1,300macroglobulinemiaNodal marginal zone B-cell lymphoma*<2%1,300Splenic marginal zone B-cell lymphoma*<1%650Intravascular large B-cell lymphoma<1%650Primary effusion lymphoma<1%650Burkitt Lymphoma2%1,650Lymphomatoid granulomatosis<1%650Non Hodgkin's Lymphoma (NHL): T-cell and natural killer cell10%8,330Multiple Myeloma (MM)N/A19,920*A type of marginal zone lymphoma (MZL)
NHL displays marked heterogeneity recognized at the clinical, pathologic, and genetic levels (2), with the most prevalent being DLBCL, FL, marginal zone lymphomas (MZL, including all three subgroups), CLL/SLL, and MCL. (Good D J, Gascoyne R D., “Classification of non-Hodgkin's lymphoma,” Hematol. Oncol. Clin. North Am.” 2008, 22(5):781-805, vii.) Overall, mature B-cell neoplasms account for approximately 6% of all cancers in the US, with a male predominance (Jemal A, Siegel R, Ward E, et al. “Cancer statistics, 2008,” CA Cancer J. Clin., 2008, 58(2):71-96). NHL is the fifth most prevalent cancer in both males and females. In 2008, the expected number of deaths from these mature B-cell-derived neoplasms was 28,780. Of this number, 4.7% are deaths due to Hodgkin's Lymphoma, 58.2% are due to B-cell NHL, and 37.1% are due to MM.
Diagnosis of mature B-cell neoplasms relies mostly on the pathologic examination of biopsy material (be it either of an incisional or excisional biopsy of a suspect lymph node), a fine needle aspirate of a suspect lymph node (as yet to be considered adequate for initial diagnosis, unless it is the only safe option), or a bone marrow aspirate. Biopsy material is also evaluated for immunophenotype by flow cytometry, for expression of protein markers by immunohistochemistry, for B-cell clonality by IGH rearrangement analysis by PCR, or Southern blotting of genomic DNA, and for the presence of chromosomal abnormalities associated with a specific lymphoma subtype usually by fluorescence in-situ hybridization (FISH) or by PCR. Unlike other cancers, rarely are other biopsy/surgical procedures performed prior to the initiation of treatment, thus limiting the amount of tissue available for diagnostic and prognostic purposes. Few, if any, robust prognostic biomarkers exist for these neoplasms with the exception of CLL/SLL as described herein.
Thus, overall, only few chromosomal/genetic abnormalities are utilized in a clinical laboratory setting to assist in the diagnosis and prognosis of mature B-cell neoplasms. Those were mostly recognized initially through traditional cytogenetic studies and often now are assessed by molecular cytogenetic techniques such as FISH or by molecular genetic techniques such as PCR. Over the years, however, much research effort has been expended in order to identify robust biomarkers of this group of diseases at the DNA, RNA, and protein levels. It was not until genome scanning technologies such as comparative genomic hybridization (CGH) using firstly metaphase chromosomes as hybridization targets and then genome-representative BACs and oligonucleotides as targets, were introduced and became reliable, did the role of genomic gain and loss in lymphoid neoplasms become apparent (Carrasco D R, Tonon G, Huang Y, et al., “High-resolution genomic profiles define distinct clinicopathogenetic subgroups of multiple myeloma patients,” Cancer Cell, 2006, 9(4):313-25; Chen W, Houldsworth J, Olshen A B, et al., “Array comparative genomic hybridization reveals genomic copy number changes associated with outcome in diffuse large B-cell lymphomas,” Blood, 2006, 107(6):2477-85; Cheung K J, Shah S P, Steidl C, et al., “Genome-wide profiling of follicular lymphoma by array comparative genomic hybridization reveals prognostically significant DNA copy number imbalances,” Blood, 2009, 113(1):137-48; Grubor V, Krasnitz A, Troge J E, et al., “Novel genomic alterations and clonal evolution in chronic lymphocytic leukemia revealed by representational oligonucleotide microarray analysis (ROMA),” Blood, 2009, 113(6):1294-303; Jardin F, Ruminy P, Kerckaert J P, et al., “Detection of somatic quantitative genetic alterations by multiplex polymerase chain reaction for the prediction of outcome in diffuse large B-cell lymphomas,” Haematologica, 2008, 93(4):543-50; Kim W S, Honma K, Kaman S, et al., “Genome-wide array-based comparative genomic hybridization of ocular marginal zone B cell lymphoma: comparison with pulmonary and nodal marginal zone B cell lymphoma,” Genes Chromosomes Cancer, 2007, 46(8):776-83; Largo C, Saez B, Alvarez S, et al., “Multiple myeloma primary cells show a highly rearranged unbalanced genome with amplifications and homozygous deletions irrespective of the presence of immunoglobulin-related chromosome translocations,” Haematologica, 2007, 92(6):795-802; Lehmann S, Ogawa S, Raynaud S D, et al., “Molecular allelokaryotyping of early-stage, untreated chronic lymphocytic leukemia,” Cancer, 2008, 112(6):1296-305; Lenz G, Wright G W, Emre N C, et al., “Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways,” Proc. Natl. Acad. Sci. U.S.A., 2008, 105(36):13520-5; Martinez-Climent J A, Alizadeh A A, Segraves R, et al., “Transformation of follicular lymphoma to diffuse large cell lymphoma is associated with a heterogeneous set of DNA copy number and gene expression alterations,” Blood, 2003, 101(8):3109-17; Patel A, Kang S H, Lennon P A, et al., “Validation of a targeted DNA microarray for the clinical evaluation of recurrent abnormalities in chronic lymphocytic leukemia,” Am. J. Hematol., 2008, 83(7):540-6; Ross C W, Ouillette P D, Saddler C M, Shedden K A, Malek S N, “Comprehensive analysis of copy number and allele status identifies multiple chromosome defects underlying follicular lymphoma pathogenesis,” Clin. Cancer Res., 2007, 13(16):4777-85; Rubio-Moscardo F, Climent J, Siebert R, et al., “Mantle-cell lymphoma genotypes identified with CGH to BAC microarrays define a leukemic subgroup of disease and predict patient outcome,” Blood, 2005, 105(11):4445-54; Sanchez-Izquierdo D, Buchonnet G, Siebert R, et al., “MALT1 is deregulated by both chromosomal translocation and amplification in B-cell non-Hodgkin lymphoma,” Blood, 2003, 101(11):4539-46; Schraders M, Jares P, Bea S, et al., “Integrated genomic and expression profiling in mantle cell lymphoma: identification of gene-dosage regulated candidate genes,” Br. J. Haematol., 2008, 143(2):210-21; Schwaenen C, Nessling M, Wessendorf S, et al., “Automated array-based genomic profiling in chronic lymphocytic leukemia: development of a clinical tool and discovery of recurrent genomic alterations,” Proc. Natl. Acad. Sci. U.S.A., 2004, 101(4):1039-44; Schwaenen C, Viardot A, Berger H, et al., “Microarray-based genomic profiling reveals novel genomic aberrations in follicular lymphoma which associate with patient survival and gene expression status,” Genes Chromosomes Cancer, 2009, 48(1):39-54; Tagawa H, Suguro M, Tsuzuki S, et al., “Comparison of genome profiles for identification of distinct subgroups of diffuse large B-cell lymphoma,” Blood, 2005, 106(5):1770-7; Takeuchi I, Tagawa H, Tsujikawa A, et al., “The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis,” Haematologica, 2009, 94(1):61-9; Walker B A, Leone P E, Jenner M W, et al., “Integration of global SNP-based mapping and expression arrays reveals key regions, mechanisms, and genes important in the pathogenesis of multiple myeloma,” Blood, 2006, 108(5):1733-43).
Technologies have evolved for the examination of chromosome abnormalities with differing technical advantages/disadvantages. Examples are shown below in Table 2 (Bejjani B A, Shaffer L G, “Clinical utility of contemporary molecular cytogenetics,” Annu. Rev. Genomics Hum. Genet., 2008, 9:71-86).
TABLE 2Common Technologies for Genomic Aberration DetectionTechniqueResolutionCoverageAberrations DetectedKaryotype  >10 MbpWhole genomeRearrangement (balanced, unbalanced), gain, lossSKY  >2 MbpWhole genomeRearrangement (balanced, unbalanced), gain, lossChromosomal-CGH  >2 MbpWhole genomeGain, lossFISH  >20 kbpProbe-specificRearrangement (balanced, unbalanced), gain, lossArray-CGH5-100 kbp*Whole genomeRearrangement (unbalanced), gain, lossSNP-Array   5 kbpWhole genomeGain, loss, uniparental disomy, mutationPCR  <10 kbpGene-specificRearrangement (balanced, unbalanced), gain, loss, mutationSouthern Blotting  <20 kbpGene-specificRearrangement (balanced, unbalanced), gain, loss*5 kbp for oligonucleotide targets, 100 kbp for BAC targets
The above technologies provide limited usefulness and require the following considerations: karyotyping and FISH are labor-intensive; SKY, chromosomal-CGH, FISH, array-CGH, and SNP-array require costly reagents/equipment; karyotyping requires growth of cells; chromosomal-CGH, array-CGH, SNP-array, PCR, and Southern blotting only require DNA, chromosomal-CGH, array-CGH, and SNP-array require algorithmic analysis; PCR, FISH, and Southern blotting afford the greatest sensitivity. In a clinical diagnostic setting, karyotyping, FISH, PCR, and to a much reduced extent Southern blotting, have been the technologies of choice, and the American College of Medical Genetics (ACMG) has established Standards and Guidelines for these technologies. Standards and Guidelines have been suggested for the performance of array-CGH as a replacement for (or as an adjunct to) standard cytogenetic techniques (e.g., karyotyping, FISH) as commercially available FDA-approved devices, as commercially available Investigational Use Only (IUO) devices requiring validation, or as “home-brew” or in-house developed and validated devices; however, they have not yet been adopted. To date, array-CGH has been utilized primarily as “home-brew” assays.
With increasing resolving power afforded by oligonucleotide arrays, smaller recurrent gains and losses have been identified and common regions of genomic gain/loss have been narrowed. Few alterations have been reported to be associated with the disease or a biologic or clinical feature of the disease. Thus, in mature B-cell neoplasms, the biologic role of genomic gain and loss is still in the discovery phase and the full potential of genomic gain/loss as diagnosticators and prognosticators for the diseases has yet to be explored and exploited in a clinical setting.